Carbon dioxide capture technologies have become a focus to overcome global warming. Biphasic absorbents are one of the promising approaches for energy-saving CO₂ capture processes. These biphasic absorbents are mainly composed of a mixed solvent composed of alkanolamine and organic solvents like glycol ether or alcohol. However, screening experiments of the mixed-solvent absorbents are required to search for biphasic absorbents due to their complicated phase behavior. In this work, we developed a prediction method for the phase states of the mixed-solvent absorbents using a quantum calculation and machine learning models, including random forest, logistic regression, and support vector machine models. There are 61 mixed-solvent absorbents containing alkanolamine/glycol ether or alcohol in the dataset. The machine learning models successfully predicted the phase states of the mixed-solvent absorbents before and after CO₂ absorption with accuracies of more than 90%. Furthermore, we analyzed the contributions of explanatory variables for prediction using the learned model. As a result, we found that molecular surface charge of the amine species is more important than those of the other organic solvents to determine the phase behaviors during CO₂ absorption.

A new class of triple-staged, thermosensitive hydrogels has been developed using double cross-linking between succinyl chitosan (SC) and poly(N-isopropylacrylamide-co-5-methacrylamido-1,2-benzoxaborole) and poly(NIPAM-co-MAAmBO) copolymers. Poly(NIPAM-co-MAAmBO) was first synthesized via RAFT polymerization with various monomer ratios and cross-linked by the hydroxyl groups on chitosan with poly(MAAmBO) segments to form the boronate ester bonding. Combining with the thermoresponsive poly(NIPAM) segments, we successfully prepared a dual-cross-linked hydrogel demonstrating a unique triple-staged, temperature-induced, sol–gel–sol transition behavior. This hydrogel exhibits injectability and self-healing capabilities. The mechanical properties of hydrogels can be tuned by adjusting the copolymer structure, hydrogel composition, temperature, and pH conditions. Among the various formulations, the hydrogel with a composition of SC/poly(NIPAM₉₀-co-MAAmBO₁₀) = 5/5 (w/w) exhibited superior mechanical properties, with a storage modulus (G′) of approximately 1000 Pa at pH 7.4 and 37 °C. Rheological analysis confirmed the stability, pH responsiveness, and reversible temperature-induced sol–gel–sol transitions of the hydrogel. In summary, we demonstrated that the microstructure and properties of the SC/poly(NIPAM-co-MAAmBO) hydrogels could be tailored through dynamic boronate ester bonds and temperature-responsive hydrogen bonds, showcasing significant potential for biomedical applications.

Erythromycin (ERY), an antibiotic widely used in human and veterinary medicine, persists in the environment due to its low degradability, accumulating in wastewater and soil. This study presents a novel adsorbent synthesized by magnetically modifying calcined natural bentonite with Fe₃O₄ nanoparticles to enhance ERY removal. The modification increased the surface area, with the highest adsorption observed at pH 11. Adsorption studies revealed that the Dubinin–Radushkevich isotherm model and pseudo-first-order kinetic model best described the adsorption behavior. Response surface methodology (RSM) was employed to optimize key parameters, including adsorbent dosage, temperature, and contact time. The quadratic model indicated optimal conditions of 41.9 mg adsorbent, 29.1 °C, and 9.6 h of contact time, yielding a maximum ERY removal efficiency of 96.2%. Density functional theory (DFT) analysis provided a molecular-level understanding of the adsorption mechanism, identifying strong interactions between ERY, Fe₃O₄, and bentonite. The theoretical binding energy aligns with experimental results, confirming the role of magnetic modification in promoting ERY adsorption. This study demonstrates a promising approach for mitigating ERY contamination in aqueous environments.